Narrow Results

Having achieved an export-led exponential economic growth, Singapore remains vulnerable to both natural disasters and economic crises. However, the economic repercussions and policy responses to extreme events for an island nation like Singapore are not as widely known or studied. This paper illustrates that impacts of a health disaster [Severe Acute Respiratory Syndrome (SARS)] and an economic crisis [Global Financial Crisis (GFC)] on the Singapore economy based on selected indicators of the financial market, macroeconomy and property sector. Crises of different nature entail different policy responses of different scales and this is highlighted in the policy responses to both SARS and GFC toward economic recovery. In the case of SARS, there were preventive measures toward diseases but no reactive measures as the SARS virus was a new strain. For GFC, the policy measures were simply reactive as preventive measures failed to regulate the financial markets effectively. Our paper makes the case that the impacts of such extreme events are systemic as they affect all aspects of Singaporean society and that, moreover, the island nation is more vulnerable to these shocks than is currently acknowledged.

The recent turbulence on the world's stock markets has reinvigorated the attack on classical economic models of stock market fluctuations. The key problem is determining a dynamic model, which is consistent with observed fluctuations and which reflects investor behavior. Here, we use a novel equation-free approach developed in nonlinear dynamics literature to identify the salient statistical features of fluctuations of the Dow Jones Industrial Average over the past 80 years. We then develop a minimal dynamical model in the form of a stochastic differential equation involving both additive and multiplicative system-noise couplings, which captures these features and whose parameterization on a time scale of days can be used to capture market distributions up to a time scale of months. The terms in the model can be directly linked to "herding" behavior on the part of traders. However, we show that parameters in this model have changed over a number of decades producing different market regimes. This result partially explains how, during some periods of history, "classic" economic models may work well and at other periods "econo-physics" models prove better.

In this study, we investigate the occurrence of dragon-king extreme events in a three-dimensional autonomous Shimizu–Morioka oscillator. We observe that the bounded chaotic oscillations transition into large amplitude extreme events at a critical value of the system control parameter triggered by an interior crisis. These extreme events exhibit a unique distribution characterized by the probability distribution function. We performed laboratory experiments and conducted rigorous numerical simulations on the Shimizu–Morioka oscillator to validate our findings. The results from both approaches are in excellent agreement and confirm extreme behavior in this autonomous system. Our study represents the first comprehensive investigation of extreme events in the Shimizu–Morioka oscillator, integrating experimental observations and numerical simulations. Also, we observed the dragon-king extreme events in both experimental and numerical studies. These findings enhance our understanding of extreme events and their potential applications in chaos-based dynamical systems, contributing to advancing this field.

In this work, we study the multifractal properties of the return intervals between fading events defined as laser intensity drops induced by optical turbulence. A laboratory-controlled experiment was conducted in which the irradiance fluctuations were recorded at a high sampling rate. Return intervals were obtained by identifying fading events under a threshold defined in times of the standard deviation of the measured irradiance. Using multifractal detrended fluctuation analysis, we found a crossover between two different scaling regimes: a spurious multifractal slightly correlated at small scales and a monofractal correlated for large scales. Based on a surrogate analysis and numerical simulations of monofractal processes, we conclude the heavy tail of the probability distribution of the return interval is the origin of the multifractality observed at small scales.

Hurricane Sandy arrived on October 29, 2012 with enormous adverse impacts on one of the largest metropolitan areas in the world, the New York region, including its electric power and transportation systems that disrupted the social and economic fabric of a wide geographic area. How these services were restored is an important basis for proactively designing robust public services not only in areas affected by Hurricane Sandy but also for many similar areas and events worldwide. Recovery rates are one important indicator of consequences of disaster. Selected physical and social measures of recovery are analyzed for New York City electricity and rail transit services following Hurricane Sandy to capture the ability of these services to be restored over various time periods, and to illustrate the use of indicators of restoration for infrastructure service resilience analyses for disaster management and planning. Electricity impacts and recovery are presented in user-oriented terms as the ratio of customers affected and restored to both customers without service and total customers served in the period following the hurricane. Results showed that restoration rates varied among the boroughs of NYC potentially reflecting differences in infrastructure resilience and exposure conditions. Rail transit impacts and recovery were analyzed in terms of both the restoration of the two dozen subway lines as a physical measure and ridership by station as a user or social measure. Line recovery analyses showed that within about a couple of weeks of the storm almost 80 percent of the lines were fully restored, almost 20 percent were partially restored, and complete suspensions were rare indicating a high degree of restoration capacity. Ridership measured at stations was compared to levels and rates of change over various time periods around the storm period, as well as from the previous year. High correlations in ridership between single days before and after the storm indicated little redistribution of ridership across stations between those time periods, however, rates of change indicated about a 14–16 percent decline in ridership system-wide within a two week period following the storm relative to the same time period the year before, recovering later, possibly reflecting slower rates of social recovery relative to the recovery of the physical transit systems. A longer period of service quality effects occurred due to lingering equipment damage potentially attributed for example to water damage and corrosion from saltwater. These findings point to the importance of recovery time as at least an initial guide to ways of systematizing service restoration measures and are a foundation for physical, social, and institutional adaptation strategies some of which are already being put in place. Larger considerations remain however in interpreting recovery restoration goals and who decides them.

The purpose of this study was to examine stakeholder perceptions of climate change and local adaptation strategies in the New York City area. A side-by-side comparison of expert and resident opinions provided a clear picture of the region's climate change attitude in the year following Superstorm Sandy. Semi-structured interviews with regional environmental experts provided material for a structured survey, which was then distributed to 100 experts and 250 residents in coastal NY and northern NJ counties. In the survey both stakeholder groups were asked to choose the top three climate threats to the NYC region and rate adaptation and mitigation strategies on a 1–5 Likert scale regarding their ability to protect the region and their cost-effectiveness. Results show that experts and residents agree that sea level rise, coastal flooding and storm surge, and an increased frequency and intensity of extreme events pose the greatest threats to NYC over the next 25 years. While both groups showed a preference for long-term planning over immediate action, experts and residents could not agree on which specific strategies would best serve the region. The aftermath of Superstorm Sandy had a strong impact on both the expert and resident opinions and efforts to monitor stakeholder opinions continue.

Extreme daily and transient precipitation events have been on the rise in the continental United States. These changes have the potential to disrupt human and natural systems. Tree rings can reconstruct annual estimates of past climate, but have limitations in reconstructing extreme precipitation events. We analyzed instrumental records to evaluate patterns in daily, weekly, and seasonal precipitation in four regions spanning a climatic gradient in the eastern United States. Relationships between tree-ring reconstructions of hydroclimate and precipitation events were analyzed to characterize extreme years over the last 1000 years. From 1944–2013, the Hudson Valley and Ohio Valley regions have experienced wetter summers as well as an increase in the frequency of daily rainfall. Coinciding with these increases, half or more of the extreme wet years in these two regions have occurred in the last 20 years. Significant differences in the structure of weekly growing-season precipitation between extreme wet and dry years were found in late May and late June in the Ohio Valley and early June in the Mississippi Valley, with negligible differences in the northern and southern ends of our gradient. We also found dry-spell duration was significantly different between extreme wet and dry years in all regions except for the northern end of our study gradient. In contrast, dry-spell frequency was significantly different between extreme wet and dry years in all regions except for the southern end of our gradient. Reconstructed Palmer Drought Severity Index (PDSI) was significantly and positively correlated with total summer rainfall and significantly and negatively correlated with rainless-day frequency in all regions, with stronger correlations during extreme years. Working in the region with the strongest relations, we reconstructed summer precipitation and summer rainless days in the Hudson Valley back to 1525 CE and 1625 CE, respectively. From these reconstructions, we infer that the 20th century is characterized by more extreme summer precipitation totals and a decline in rainless days with 75.8% of the last 33 years having fewer dry days than the 377-year mean. The forecasted changes toward longer, more intense droughts over the next century are not yet realized in our study regions. However, should these shifts occur, human and natural systems will likely undergo abrupt change in response to alterations in hydrology, ecological disturbance, and terrestrial productivity, with the Northeast potentially being most vulnerable.

People living in communities surrounding the Indian Point Nuclear Generating Plant (IPNGP) inevitably face potential risks associated with a nuclear plant issue, which could become a potential extreme event. The study searches for answers to the three research questions: (1) What is the population makeup of those residing nearby the IPNGP (2) Which racial and ethnic groups are more vulnerable to the potential risks associated with the IPNGP (3) Who would be exposed to the radioactive plume if the IPNGP experienced a core-meltdown accident? The study utilizes Radiological Assessment System for Consequence Analysis (RASCAL) computer software code to project the radioactive plume dispersion when the IPNGP encounters a core-meltdown accident. Findings show that there are between 1.39 million and 19.85 million people that could possibly be exposed to the radioactive plume under four different typifying weather conditions in a calendar year. The study suggests this potential risk of an extreme event should be taken into consideration for “flexible adaptation approach” for building a resilient and sustainable New York City, rather than the one where a serious disaster could impact thousands of lives, create millions of dollars in damage, and create another scenario where emergency response is tested and fails to meet the requirements of appropriate sources to respond and do their part accordingly and appropriately to protect and save the lives of residents.

The importance of critical infrastructures and strategic planning in the context of extreme events, climate change and urbanization has been underscored recently in international policy frameworks, such as the Sustainable Development Goals (SDGs), the Sendai Framework for Disaster Risk Reduction 2015–2030 (UNISDR (United Nations/International Strategy for Disaster Risk Reduction) 2015), and the new Paris climate agreement (UNFCCC (United Nations — Framework Convention on Climate Change) 2015) as well as the New Urban Agenda (UN-HABITAT 2016). This paper outlines key research challenges in addressing the nexus between extreme weather events, critical infrastructure resilience, human vulnerability and strategic planning. Using a structured expert dialogue approach (particularly based on a roundtable discussion funded by the German National Science Foundation (DFG)), the paper outlines emerging research issues in the context of extreme events, critical infrastructures, human vulnerability and strategic planning, providing perspectives for inter- and transdisciplinary research on this important nexus. The main contribution of the paper is a compilation of identified research gaps and needs from an interdisciplinary perspective including the lack of integration across subjects and mismatches between different concepts and schools of thought.

Any adaptation activity needs a reliable evidence basis for the climate itself as well as for the exposition and sensitivity of the social, economic or ecological system and its elements. This requires an assessment of recent climate impacts as well as potential future climate change impacts in order to select tailor-made adaptation measures. For a methodologically coherent assessment, the Intergovernmental Panel on Climate Change (IPCC) had introduced the requirement of a parallel modeling approach which means that demographic and socioeconomic changes are projected in parallel to the changes of the climatic system. This paper discusses a conceptual framework of a parallel modeling approach and presents its application in four case studies of climate change impact assessments in Germany, covering the national, regional and local scale. The results from the different applications prove the hypothesis that the change in sensitivity (i.e., demographic change, economic change and change in land-use patterns) often determines the magnitude of climate- and weather-related impacts in the near future significantly. The case studies, however, also show that adaptation processes have to be organized in a collaborative way, which takes the knowledge, and also the concerns of the addressees into full account. A broad mandate from all social groups is especially needed when political decisions are based on uncertain knowledge — which is the case whenever climate change impacts are assessed.

This study examines how three types of extreme events (heat waves, droughts, floods) are mentioned together with climate change on social media. English-language Twitter use during 2008–2017 is analyzed, based on 1,127,996 tweets (including retweets). Frequencies and spikes of activity are compared and theoretically interpreted as reflecting complex relations between the extreme event factor (the occurrence of an extreme event); the media ecology factor (climate-change oriented statements/actions in the overall media landscape) and the digital action factor (activities on Twitter). Flooding was found to be by far the most tweeted of the three in connection to climate change, followed by droughts and heat waves. It also led when comparing spikes of activity. The dominance of floods is highly prevalent from 2014 onwards, triggered by flooding events (extreme event factor), the climate science controversy in US politics (media ecology factor) and the viral power of celebrities’ tweets (digital action factor).

During early 2020, the world encountered an extreme event in the form of a new and deadly disease, COVID-19. Over the next two years, the pandemic brought sickness and death to countries and their cities around the globe. One of the first and initially the hardest hit location was New York City, USA. This article is an introduction to the Special Issue in this journal that highlights the impacts from and responses to COVID-19 as an extreme event in the New York City metropolitan region. We overview the aspects of COVID-19 that make it an important global extreme event, provide brief background to the conditions in the world, and the US before describing the 10 articles in the issue that focus on conditions, events and dynamics in New York City during the initial phases of the pandemic.

Ten years' hydrological investigations at Danum have provided strong evidence of the effects of extremes of drought, as in the April 1992 El Niño southern oscillation event, and flood, as in January 1996. The 1.5 km² undisturbed forest control catchment experienced a complete drying out of the stream for the whole 1.5 km of defined channel above the gauging station in 1992, but concentrated surface flow along every declivity from within a few metres of the catchment divide after the exceptional rains of 19 January 1996. Under these natural conditions, erosion is episodic. Sediment is discharged in pulses caused by storm events, collapse of debris dams and occasional landslips. Disturbance by logging accentuates this irregular regime. In the first few months following disturbance, a wave of sediment is moved by each storm, but over subsequent years, rare events scour sediment from bare areas, gullies and channel deposits. The spatial distribution of sediment sources changes with time after logging, as bare areas on slopes are revegetated and small gullies are filled with debris. Extreme storm events, as in January 1996, cause logging roads to collapse, with landslides leading to surges of sediment into channels, reactivating the pulsed sediment delivery by every storm that happened immediately after logging. These effects are not dampened out with increasing catchment scale. Even the 721 km² Sungai Segama has a sediment yield regime dominated by extreme events, the sediment yield in that single day on 19 January 1996 exceeding the annual sediment load in several previous years. In a large disturbed catchment, such road failures and logging-activity-induced mass movements increase the mud and silt in floodwaters affecting settlements downstream. Management systems require long-term sediment reduction strategies. This implies careful road design and good water movement regulation and erosion control throughout the logging process.

The most prominent feature of the time evolution of the global climate system during the last century is the increase in the globally averaged surface air temperature (SAT). According to the recent Intergovernmental Panel on Climate Change (IPCC) Special Report on global warming of 1.5°C (SR15), the global average SAT has increased at a rate of 0.2°C per decade, leading to 0.87°C higher mean temperature between 2006–2015 than that between 1850–1900, based on an estimate using multiple datasets (Allen et al., 2018). This warming change in the global climate system has been primarily attributed to emissions of various chemical gases due to human activities. The emitted chemical gases, such as carbon dioxide (CO₂), live in the atmosphere, block thermal radiation from the surface and lower atmosphere to outer space, and, as a consequence, result in an increase in SATs, which is denoted as the greenhouse effect…

The following sections are included:

• Average Returns are the Main Focus
• Compound Return Facts: Tail Risks Dominate!
• Compound Returns a Function of Risk
• Big Problem: Tail Events More Frequent than Normal Distribution
• Think Tails of the Distribution: Concentrate on Normal Events
• Relative Performance Evaluation
• Investment Strategies — Asset Allocation Static Constraints are Costly
• Factors that Affect Terminal Wealth
• Measuring Tail Risk Using Market Prices
• Tail Gains/Losses from Opiton Prices
• Enhancing Compound Returns Through Dynamic Risk Management (1996—2015)
• Using Option Prices to Forecast Risk Changing Risk Proactively
• Using Option Prices to Measure Risk
• Uncertainty of the Distribution of Returns
• Adaptive Strategy — Pre and Post “2008” Crisis
• Issues

Water resources in equatorial atolls are amongst the most vulnerable globally, partly because of extreme variability of annual precipitation (P) due to frequent ENSO events. IPCC projections for the central and western tropical Pacific indicate mean annual rainfall will increase as sea surface temperatures (SSTs) rise. Projections of the intensity and frequency of ENSO events and hydrological droughts are of low confidence. Here, relationships between 12-month May to April precipitation (P_M-A) in two equatorial atolls, Tarawa and Kiritimati in Kiribati, and 12-month SST_M-A in the Nino regions surrounding the atolls are examined between 1950 and 2022. Only the Nino4 region has as significant temporal trend in SST_M-A. There are no significant trends in P_M-A in either atoll due to large ENSO-related interannual variability. In both atolls, strong, highly significant correlations are found between P_M-A and SST_M-A in the Nino regions eastward of the atolls. These show ∂P_M-A/∂SST_M-A for both atolls over 1,000 mm/°C. The relationship for Kiritimati appears nonlinear. Comparison of ranked very much above normal (VMAN, percentile > 0.9) and very much below normal (VMBN, percentile < 0.1) SST_M-A with ranked VMAN and VMBN P_M-A revealed poor correspondence for both atolls, suggesting that extreme SST_M-A are not a sole determinant of extreme P_M-A. The nonlinearity of the relation is shown by ∂P_M-A/∂SST_M-A for below normal P_M-A being an order of magnitude smaller than trends for above normal P_M-A.

Prediction of epileptic seizures relies on a consistent relationship between indices of brain activity on the one hand and seizure probabilities on the other hand. We study a qualitative complex network model of integrate-and-fire neurons which generates both asynchronous behavior and short seizure-like events of collective firing. The network model is interpreted as a small seizure-generating zone, which is influenced by various endogenous and exogenous factors, which possibly modulate seizure probability and which are represented by model parameters. For constant parameters the events seem unpredictable, and we find that an increase in seizure probability is accompanied by an increase of fluctuations of the asynchronous behavior, for various considered influencing factors. Using this observation, we find some predictability if the model is endowed with randomly varying model parameters, which could reflect changes in endogenous and exogenous factors that might influence ictogenesis.